Controlling a process for inspection of a specimen

ABSTRACT

Methods and systems for controlling a process for inspection of a specimen are provided. One system includes one or more computer subsystems configured for determining a statistical characteristic of difference images generated for multiple instances of a care area on a specimen and determining variation in the statistical characteristic compared to a statistical characteristic of difference images generated for multiple instances of the care area on one or more other specimens. In addition, the one or more computer subsystems are configured for determining one or more changes to one or more parameters used for detecting defects in the care area on the specimen based on the variation.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention generally relates to methods and systems forcontrolling a process for inspection of a specimen.

2. Description of the Related Art

The following description and examples are not admitted to be prior artby virtue of their inclusion in this section.

Inspection processes are used at various steps during a semiconductormanufacturing process to detect defects on reticles and wafers topromote higher yield in the manufacturing process and thus higherprofits. Inspection has always been an important part of fabricatingsemiconductor devices. However, as the dimensions of semiconductordevices decrease, inspection becomes even more important to thesuccessful manufacture of acceptable semiconductor devices becausesmaller defects can cause the devices to fail.

“Care areas” as they are commonly referred to in the art are areas on aspecimen that are of interest for inspection purposes. Sometimes, careareas are used to differentiate between areas on the specimen that areinspected from areas on the specimen that are not inspected in aninspection process. In addition, care areas are sometimes used todifferentiate between areas on the specimen that are to be inspectedwith one or more different parameters. For example, if a first area of aspecimen is more critical than a second area on the specimen, the firstarea may be inspected with a higher sensitivity than the second area sothat defects are detected in the first area with a higher sensitivity.Other parameters of an inspection process can be altered from care areato care area in a similar manner.

Different categories of inspection care areas are currently used. Onecategory is legacy care areas, which are traditionally hand drawn. Withnearly all users adopting design guided inspection, very few legacy careareas are currently used. Another category is design based care areas.These are care areas derived based on heuristics on chip design patternsprinted on the specimen. The user tries to look at the chip design andderive methods/scripts that will help derive care areas. There aremultiple techniques and tools available to define these design basedcare areas. As they are derived from ground truth (chip design), theycan provide high precision, substantially tiny care areas and also allowinspection systems to store high volumes of care areas. These care areasare important not just from a defect detection standpoint, but they areoften crucial to noise suppression.

Some currently used inspection methods also use regular groups of careareas in which care areas of different noise behavior are groupedtogether and even one single care area can include many differentstructures of different noise behavior. In order to identify areas inwhich the noise is higher, several iterations of a so-calleddesign-based search has to be performed over and over. This proceduretakes a lot of time.

The currently used methods and systems for inspection involving careareas have, therefore, a number of disadvantages. For example, the timeto results is substantially slow as several iterations of searching fornoisy structures have to be performed. Sometimes, it is impossible toidentify all the noisy structures manually due to complexity. In thiscase, the sensitivity used to inspect areas that are less noisy iscompromised because areas that are more noisy are falling in the samecare area group. This diminished inspection sensitivity can prevent thefinding of key defects of interest (DOIs).

Defect detection is commonly performed based on difference imagesgenerated by subtracting a reference from a test image. In some cases,the difference image grey level is used directly to create atwo-dimensional (2D) histogram after which outliers are identified.Those outliers can be DOIs or nuisance events. In some cases, aso-called difference filter is applied to the difference image meaningthe difference image is convolved with the matrix of the differencefilter. In general, with and without a difference filter, thecalculation of the difference image can be viewed as static and nodifferences between wafers are considered in its calculation.

The currently used inspection methods and systems can, therefore, haveadditional disadvantages. For example, when the specimen fabricationprocess has been changed, the sensitivity of the inspection iscompromised, and the inspection recipe has to be re-tuned. In addition,all attributes are used for recipe tuning no matter how stable they arewhen process variation is considered. Furthermore, changes in theillumination parameters such as illumination and gain are not consideredwhen performing defect detection.

Accordingly, it would be advantageous to develop systems and methods forcontrolling a process for inspection of a specimen that do not have oneor more of the disadvantages described above.

SUMMARY OF THE INVENTION

The following description of various embodiments is not to be construedin any way as limiting the subject matter of the appended claims.

One embodiment relates to a system configured for controlling a processfor inspection of a specimen. The system includes an inspectionsubsystem configured to generate output responsive to energy detectedfrom a specimen. The system also includes one or more computersubsystems configured for generating difference images for multipleinstances of a care area on the specimen by subtracting a reference fromthe output corresponding to the multiple instances of the care area. Theone or more computer subsystems are also configured for determining astatistical characteristic of the difference images for the multipleinstances of the care area. In addition, the one or more computersubsystems are configured for determining variation in the statisticalcharacteristic compared to a statistical characteristic of differenceimages generated for multiple instances of the care area on one or moreother specimens. The one or more computer subsystems are furtherconfigured for determining one or more changes to one or more parametersused for detecting defects in the care area on the specimen based on thevariation. The system may be further configured as described herein.

Another embodiment relates to a computer-implemented method forcontrolling a process for inspection of a specimen. The method includesgenerating difference images for multiple instances of a care area on aspecimen by subtracting a reference from output corresponding to themultiple instances of the care area. The output is generated by aninspection subsystem and is responsive to energy detected from thespecimen. The method also includes the determining a statisticalcharacteristic, determining variation, and determining one or morechanges steps described above, which are performed by one or morecomputer subsystems.

Each of the steps of the method described above may be performed asdescribed further herein. In addition, the embodiment of the methoddescribed above may include any other step(s) of any other method(s)described herein. Furthermore, the method described above may beperformed by any of the systems described herein.

Another embodiment relates to a non-transitory computer-readable mediumstoring program instructions executable on a computer system forperforming a computer-implemented method for controlling a process forinspection of a specimen. The computer-implemented method includes thesteps of the method described above. The computer-readable medium may befurther configured as described herein. The steps of thecomputer-implemented method may be performed as described furtherherein. In addition, the computer-implemented method for which theprogram instructions are executable may include any other step(s) of anyother method(s) described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Further advantages of the present invention will become apparent tothose skilled in the art with the benefit of the following detaileddescription of the preferred embodiments and upon reference to theaccompanying drawings in which:

FIGS. 1 and 2 are schematic diagrams illustrating side views ofembodiments of a system configured as described herein;

FIG. 3 is a flow chart illustrating an embodiment of steps performed bythe one or more computer subsystems described herein;

FIG. 4 is a schematic diagram illustrating a plan view of one example ofa care area on a specimen and a chart showing results of a projectionperformed for polygons in the care area;

FIG. 5 is a schematic diagram illustrating a plan view of one example ofa care area on a specimen;

FIG. 6 is an example of a two-dimensional histogram generated fromdifferent values determined from output generated by a detector of aninspection subsystem for the polygons in the care area shown in FIG. 5;

FIG. 7 is a schematic diagram illustrating a plan view of the care areashown in FIG. 5 with the polygons in the care area separated intoinitial sub-groups based on a characteristic of the polygons;

FIG. 8 includes examples of two-dimensional histograms generated fromdifferent values determined from output generated by a detector of aninspection subsystem for the initial sub-groups of polygons shown inFIG. 7 and differences in the two-dimensional histograms;

FIG. 9 is a schematic diagram illustrating a plan view of the care areashown in FIG. 5 with the polygons in the care area separated into finalsub-groups based on the similarities and differences in thetwo-dimensional histograms shown in FIG. 8;

FIG. 10 is a block diagram illustrating one embodiment of anon-transitory computer-readable medium storing program instructions forcausing a computer system to perform a computer-implemented methoddescribed herein;

FIG. 11 is a flow chart illustrating one embodiment of steps that may beperformed by the embodiments described herein for controlling a processfor inspection of a specimen; and

FIG. 12 is an example of a whisker plot that may be generated by theembodiments described herein for controlling a process for inspection ofa specimen.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and are herein described in detail. The drawingsmay not be to scale. It should be understood, however, that the drawingsand detailed description thereto are not intended to limit the inventionto the particular form disclosed, but on the contrary, the intention isto cover all modifications, equivalents and alternatives falling withinthe spirit and scope of the present invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

“Nuisances” (which is sometimes used interchangeably with “nuisancedefects”) as that term is used herein is generally defined as defectsthat a user does not care about and/or events that are detected on aspecimen but are not really actual defects on the specimen. Nuisancesthat are not actually defects may be detected as events due tonon-defect noise sources on a specimen (e.g., grain in metal lines onthe specimen, signals from underlaying layers or materials on thespecimen, line edge roughness (LER), relatively small critical dimension(CD) variation in patterned attributes, thickness variations, etc.)and/or due to marginalities in the inspection system itself or itsconfiguration used for inspection.

The term “defects of interest (DOIs)” as used herein is defined asdefects that are detected on a specimen and are really actual defects onthe specimen. Therefore, the DOIs are of interest to a user becauseusers generally care about how many and what kind of actual defects areon specimens being inspected. In some contexts, the term “DOI” is usedto refer to a subset of all of the actual defects on the specimen, whichincludes only the actual defects that a user cares about. For example,there may be multiple types of DOIs on any given specimen, and one ormore of them may be of greater interest to a user than one or more othertypes. In the context of the embodiments described herein, however, theterm “DOIs” is used to refer to any and all real defects on a specimen.

The terms “design” and “design data” as used herein generally refer tothe physical design (layout) of an IC and data derived from the physicaldesign through complex simulation or simple geometric and Booleanoperations. The physical design may be stored in a data structure suchas a graphical data stream (GDS) file, any other standardmachine-readable file, any other suitable file known in the art, and adesign database. A GDSII file is one of a class of files used for therepresentation of design layout data. Other examples of such filesinclude GL1 and OASIS files and proprietary file formats such as RDFdata, which is proprietary to KLA, Milpitas, Calif. In addition, animage of a reticle acquired by a reticle inspection system and/orderivatives thereof can be used as a “proxy” or “proxies” for thedesign. Such a reticle image or a derivative thereof can serve as asubstitute for the design layout in any embodiments described hereinthat use a design. The design may include any other design data ordesign data proxies described in commonly owned U.S. Pat. No. 7,570,796issued on Aug. 4, 2009 to Zafar et al. and 7,676,077 issued on Mar. 9,2010 to Kulkarni et al., both of which are incorporated by reference asif fully set forth herein. In addition, the design data can be standardcell library data, integrated layout data, design data for one or morelayers, derivatives of the design data, and full or partial chip designdata.

In some instances, simulated or acquired images from a wafer or reticlecan be used as a proxy for the design. Image analysis can also be usedas a proxy for design data. For example, polygons in the design may beextracted from an image of a design printed on a wafer and/or reticle,assuming that the image of the wafer and/or reticle is acquired withsufficient resolution to adequately image the polygons of the design. Inaddition, the “design” and “design data” described herein refers toinformation and data that is generated by semiconductor device designersin a design process and is therefore available for use in theembodiments described herein well in advance of printing of the designon any physical wafers.

The “design” or “physical design” may also be the design as it would beideally formed on the wafer. In this manner, a design may not includefeatures of the design that would not be printed on the wafer such asoptical proximity correction (OPC) features, which are added to thedesign to enhance printing of the features on the wafer without actuallybeing printed themselves.

Turning now to the drawings, it is noted that the figures are not drawnto scale. In particular, the scale of some of the elements of thefigures is greatly exaggerated to emphasize characteristics of theelements. It is also noted that the figures are not drawn to the samescale. Elements shown in more than one figure that may be similarlyconfigured have been indicated using the same reference numerals. Unlessotherwise noted herein, any of the elements described and shown mayinclude any suitable commercially available elements.

One embodiment relates to a system configured for selecting defectdetection methods for inspection of a specimen. Some embodiments arerelated to statistical care area grouping for enhanced defect inspectionsensitivity. For example, the care area sub-division and statisticalre-grouping in defect inspection described herein can be used to enhancesensitivity to DOIs, to lower nuisance rate, to improve within wafer andwafer-to-wafer recipe performance stability, or some combinationthereof.

In one embodiment, the specimen is a wafer. The wafer may include anywafer known in the semiconductor arts. In another embodiment, thespecimen is a reticle. The reticle may include any reticle known in thesemiconductor arts. Although some embodiments may be described hereinwith respect to a wafer or wafers, the embodiments are not limited inthe specimen for which they can be used. For example, the embodimentsdescribed herein may be used for specimens such as reticles, flatpanels, personal computer (PC) boards, and other semiconductorspecimens.

One embodiment of such a system is shown in FIG. 1. In some embodiments,the system includes an inspection subsystem that includes at least anenergy source and a detector. The energy source is configured togenerate energy that is directed to a specimen. The detector isconfigured to detect energy from the specimen and to generate outputresponsive to the detected energy.

In one embodiment, the inspection subsystem is a light-based inspectionsubsystem. For example, in the embodiment of the system shown in FIG. 1,inspection subsystem 10 includes an illumination subsystem configured todirect light to specimen 14. The illumination subsystem includes atleast one light source. For example, as shown in FIG. 1, theillumination subsystem includes light source 16. In one embodiment, theillumination subsystem is configured to direct the light to the specimenat one or more angles of incidence, which may include one or moreoblique angles and/or one or more normal angles. For example, as shownin FIG. 1, light from light source 16 is directed through opticalelement 18 and then lens 20 to beam splitter 21, which directs the lightto specimen 14 at a normal angle of incidence. The angle of incidencemay include any suitable angle of incidence, which may vary dependingon, for instance, characteristics of the specimen and the defects to bedetected on the specimen.

The illumination subsystem may be configured to direct the light to thespecimen at different angles of incidence at different times. Forexample, the inspection subsystem may be configured to alter one or morecharacteristics of one or more elements of the illumination subsystemsuch that the light can be directed to the specimen at an angle ofincidence that is different than that shown in FIG. 1. In one suchexample, the inspection subsystem may be configured to move light source16, optical element 18, and lens 20 such that the light is directed tothe specimen at a different angle of incidence.

In some instances, the inspection subsystem may be configured to directlight to the specimen at more than one angle of incidence at the sametime. For example, the inspection subsystem may include more than oneillumination channel, one of the illumination channels may include lightsource 16, optical element 18, and lens 20 as shown in FIG. 1 andanother of the illumination channels (not shown) may include similarelements, which may be configured differently or the same, or mayinclude at least a light source and possibly one or more othercomponents such as those described further herein. If such light isdirected to the specimen at the same time as the other light, one ormore characteristics (e.g., wavelength, polarization, etc.) of the lightdirected to the specimen at different angles of incidence may bedifferent such that light resulting from illumination of the specimen atthe different angles of incidence can be discriminated from each otherat the detector(s).

In another instance, the illumination subsystem may include only onelight source (e.g., source 16 shown in FIG. 1) and light from the lightsource may be separated into different optical paths (e.g., based onwavelength, polarization, etc.) by one or more optical elements (notshown) of the illumination subsystem. Light in each of the differentoptical paths may then be directed to the specimen. Multipleillumination channels may be configured to direct light to the specimenat the same time or at different times (e.g., when differentillumination channels are used to sequentially illuminate the specimen).In another instance, the same illumination channel may be configured todirect light to the specimen with different characteristics at differenttimes. For example, in some instances, optical element 18 may beconfigured as a spectral filter and the properties of the spectralfilter can be changed in a variety of different ways (e.g., by swappingout the spectral filter) such that different wavelengths of light can bedirected to the specimen at different times. The illumination subsystemmay have any other suitable configuration known in the art for directinglight having different or the same characteristics to the specimen atdifferent or the same angles of incidence sequentially orsimultaneously.

In one embodiment, light source 16 may include a broadband plasma (BBP)light source. In this manner, the light generated by the light sourceand directed to the specimen may include broadband light. However, thelight source may include any other suitable light source such as alaser, which may be any suitable laser known in the art and may beconfigured to generate light at any suitable wavelength(s) known in theart. In addition, the laser may be configured to generate light that ismonochromatic or nearly-monochromatic. In this manner, the laser may bea narrowband laser. The light source may also include a polychromaticlight source that generates light at multiple discrete wavelengths orwavebands.

Light from optical element 18 may be focused to beam splitter 21 by lens20. Although lens 20 is shown in FIG. 1 as a single refractive opticalelement, in practice, lens may include a number of refractive and/orreflective optical elements that in combination focus the light from theoptical element to the specimen. The illumination subsystem shown inFIG. 1 and described herein may include any other suitable opticalelements (not shown). Examples of such optical elements include, but arenot limited to, polarizing component(s), spectral filter(s), spatialfilter(s), reflective optical element(s), apodizer(s), beam splitter(s),aperture(s), and the like, which may include any such suitable opticalelements known in the art. In addition, the system may be configured toalter one or more elements of the illumination subsystem based on thetype of illumination to be used for inspection.

The inspection subsystem may also include a scanning subsystemconfigured to cause the light to be scanned over the specimen. Forexample, the inspection subsystem may include stage 22 on which specimen14 is disposed during inspection. The scanning subsystem may include anysuitable mechanical and/or robotic assembly (that includes stage 22)that can be configured to move the specimen such that the light can bescanned over the specimen. In addition, or alternatively, the inspectionsubsystem may be configured such that one or more optical elements ofthe inspection subsystem perform some scanning of the light over thespecimen. The light may be scanned over the specimen in any suitablefashion.

The inspection subsystem further includes one or more detectionchannels. At least one of the one or more detection channels includes adetector configured to detect light from the specimen due toillumination of the specimen by the inspection subsystem and to generateoutput responsive to the detected light. For example, the inspectionsubsystem shown in FIG. 1 includes two detection channels, one formed bycollector 24, element 26, and detector 28 and another formed bycollector 30, element 32, and detector 34. As shown in FIG. 1, the twodetection channels are configured to collect and detect light atdifferent angles of collection. In some instances, one detection channelis configured to detect specularly reflected light, and the otherdetection channel is configured to detect light that is not specularlyreflected (e.g., scattered, diffracted, etc.) from the specimen.However, two or more of the detection channels may be configured todetect the same type of light from the specimen (e.g., specularlyreflected light). Although FIG. 1 shows an embodiment of the inspectionsubsystem that includes two detection channels, the inspection subsystemmay include a different number of detection channels (e.g., only onedetection channel or two or more detection channels). Although each ofthe collectors are shown in FIG. 1 as single refractive opticalelements, each of the collectors may include one or more refractiveoptical element(s) and/or one or more reflective optical element(s).

The one or more detection channels may include any suitable detectorsknown in the art such as photo-multiplier tubes (PMTs), charge coupleddevices (CCDs), and time delay integration (TDI) cameras. The detectorsmay also include non-imaging detectors or imaging detectors. If thedetectors are non-imaging detectors, each of the detectors may beconfigured to detect certain characteristics of the scattered light suchas intensity but may not be configured to detect such characteristics asa function of position within the imaging plane. As such, the outputthat is generated by each of the detectors included in each of thedetection channels may be signals or data, but not image signals orimage data. In such instances, a computer subsystem such as computersubsystem 36 of the system may be configured to generate images of thespecimen from the non-imaging output of the detectors. However, in otherinstances, the detectors may be configured as imaging detectors that areconfigured to generate imaging signals or image data. Therefore, thesystem may be configured to generate images in a number of ways.

It is noted that FIG. 1 is provided herein to generally illustrate aconfiguration of an inspection subsystem that may be included in thesystem embodiments described herein. Obviously, the inspection subsystemconfiguration described herein may be altered to optimize theperformance of the system as is normally performed when designing acommercial inspection system. In addition, the systems described hereinmay be implemented using an existing inspection system (e.g., by addingfunctionality described herein to an existing inspection system) such asthe 29 xx and 39 xx series of tools that are commercially available fromKLA. For some such systems, the embodiments described herein may beprovided as optional functionality of the inspection system (e.g., inaddition to other functionality of the inspection system).Alternatively, the inspection subsystem described herein may be designed“from scratch” to provide a completely new inspection system.

Computer subsystem 36 of the system may be coupled to the detectors ofthe inspection subsystem in any suitable manner (e.g., via one or moretransmission media, which may include “wired” and/or “wireless”transmission media) such that the computer subsystem can receive theoutput generated by the detectors during scanning of the specimen.Computer subsystem 36 may be configured to perform a number of functionsusing the output of the detectors as described herein and any otherfunctions described further herein. This computer subsystem may befurther configured as described herein.

This computer subsystem (as well as other computer subsystems describedherein) may also be referred to herein as computer system(s). Each ofthe computer subsystem(s) or system(s) described herein may take variousforms, including a personal computer system, image computer, mainframecomputer system, workstation, network appliance, Internet appliance, orother device. In general, the term “computer system” may be broadlydefined to encompass any device having one or more processors, whichexecutes instructions from a memory medium. The computer subsystem(s) orsystem(s) may also include any suitable processor known in the art suchas a parallel processor. In addition, the computer subsystem(s) orsystem(s) may include a computer platform with high speed processing andsoftware, either as a standalone or a networked tool.

If the system includes more than one computer subsystem, the differentcomputer subsystems may be coupled to each other such that images, data,information, instructions, etc. can be sent between the computersubsystems as described further herein. For example, computer subsystem36 may be coupled to computer subsystem(s) 102 (as shown by the dashedline in FIG. 1) by any suitable transmission media, which may includeany suitable wired and/or wireless transmission media known in the art.Two or more of such computer subsystems may also be effectively coupledby a shared computer-readable storage medium (not shown).

Although the inspection subsystem is described above as being an opticalor light-based subsystem, the inspection subsystem may be anelectron-based subsystem. For example, in one embodiment, the energydirected to the specimen includes electrons, and the energy detectedfrom the specimen includes electrons. In this manner, the energy sourcemay be an electron beam source. In one such embodiment shown in FIG. 2,the inspection subsystem includes electron column 122, which is coupledto computer subsystem 124.

As also shown in FIG. 2, the electron column includes electron beamsource 126 configured to generate electrons that are focused to specimen128 by one or more elements 130. The electron beam source may include,for example, a cathode source or emitter tip, and one or more elements130 may include, for example, a gun lens, an anode, a beam limitingaperture, a gate valve, a beam current selection aperture, an objectivelens, and a scanning subsystem, all of which may include any suchsuitable elements known in the art.

Electrons returned from the specimen (e.g., secondary electrons) may befocused by one or more elements 132 to detector 134. One or moreelements 132 may include, for example, a scanning subsystem, which maybe the same scanning subsystem included in element(s) 130.

The electron column may include any other suitable elements known in theart. In addition, the electron column may be further configured asdescribed in U.S. Pat. No. 8,664,594 issued Apr. 4, 2014 to Jiang etal., U.S. Pat. No. 8,692,204 issued Apr. 8, 2014 to Kojima et al., U.S.Pat. No. 8,698,093 issued Apr. 15, 2014 to Gubbens et al., and 8,716,662issued May 6, 2014 to MacDonald et al., which are incorporated byreference as if fully set forth herein.

Although the electron column is shown in FIG. 2 as being configured suchthat the electrons are directed to the specimen at an oblique angle ofincidence and are scattered from the specimen at another oblique angle,it is to be understood that the electron beam may be directed to andscattered from the specimen at any suitable angles. In addition, theelectron beam subsystem may be configured to use multiple modes togenerate images of the specimen (e.g., with different illuminationangles, collection angles, etc.). The multiple modes of the electronbeam subsystem may be different in any image generation parameter(s) ofthe subsystem.

Computer subsystem 124 may be coupled to detector 134 as describedabove. The detector may detect electrons returned from the surface ofthe specimen thereby forming electron beam images of the specimen. Theelectron beam images may include any suitable electron beam images.Computer subsystem 124 may be configured to perform any of the functionsdescribed herein using the output of the detector and/or the electronbeam images. Computer subsystem 124 may be configured to perform anyadditional step(s) described herein. A system that includes theinspection subsystem shown in FIG. 2 may be further configured asdescribed herein.

It is noted that FIG. 2 is provided herein to generally illustrate aconfiguration of an electron-based inspection subsystem that may beincluded in the embodiments described herein. As with the opticalsubsystem described above, the electron beam subsystem configurationdescribed herein may be altered to optimize the performance of thesubsystem as is normally performed when designing a commercialinspection system. In addition, the systems described herein may beimplemented using an existing inspection system (e.g., by addingfunctionality described herein to an existing inspection system). Forsome such systems, the embodiments described herein may be provided asoptional functionality of the system (e.g., in addition to otherfunctionality of the system). Alternatively, the system described hereinmay be designed “from scratch” to provide a completely new system.

Although the inspection subsystem is described above as being alight-based or electron beam-based subsystem, the inspection subsystemmay be an ion beam-based subsystem. Such an inspection subsystem may beconfigured as shown in FIG. 2 except that the electron beam source maybe replaced with any suitable ion beam source known in the art. In oneembodiment, therefore, the energy directed to the specimen includesions. In addition, the inspection subsystem may be any other suitableion beam-based inspection subsystem such as those included incommercially available focused ion beam (FIB) systems, helium ionmicroscopy (HIM) systems, and secondary ion mass spectroscopy (SIMS)systems.

The inspection subsystems described herein may be configured to generateoutput, e.g., images, of the specimen with multiple modes. In general, a“mode” is defined by the values of parameters of the inspectionsubsystem used for generating output and/or images of a specimen (or theoutput used to generate images of the specimen). Therefore, modes may bedifferent in the values for at least one of the parameters of theinspection subsystem (other than position on the specimen at which theoutput is generated). For example, in an optical subsystem, differentmodes may use different wavelength(s) of light for illumination. Themodes may be different in the illumination wavelength(s) as describedfurther herein (e.g., by using different light sources, differentspectral filters, etc. for different modes). In another example,different modes may use different illumination channels of the opticalsubsystem. For example, as noted above, the optical subsystem mayinclude more than one illumination channel. As such, differentillumination channels may be used for different modes. The modes mayalso or alternatively be different in one or more collection/detectionparameters of the optical subsystem. The modes may be different in anyone or more alterable parameters (e.g., illumination polarization(s),angle(s), wavelength(s), etc., detection polarization(s), angle(s),wavelength(s), etc.) of the inspection subsystem. The inspectionsubsystem may be configured to scan the specimen with the differentmodes in the same scan or different scans, e.g., depending on thecapability of using multiple modes to scan the specimen at the sametime.

In a similar manner, the output generated by the electron beam subsystemmay include output, e.g., images, generated by the electron beamsubsystem with two or more different values of a parameter of theelectron beam subsystem. The multiple modes of the electron beamsubsystem can be defined by the values of parameters of the electronbeam subsystem used for generating output and/or images for a specimen.Therefore, modes may be different in the values for at least one of theelectron beam parameters of the electron beam subsystem. For example,different modes may use different angles of incidence for illumination.

The subsystems described herein and shown in FIGS. 1 and 2 may bemodified in one or more parameters to provide different outputgeneration capability depending on the application for which they willbe used. In one such example, the inspection subsystem shown in FIG. 1may be configured to have a higher resolution if it is to be used fordefect review or metrology rather than for inspection. In other words,the embodiments of the inspection subsystems shown in FIGS. 1 and 2describe some general and various configurations for an inspectionsubsystem that can be tailored in a number of manners that will beobvious to one skilled in the art to produce inspection subsystemshaving different output generation capabilities that are more or lesssuitable for different applications.

As noted above, the optical, electron, and ion beam subsystems areconfigured for scanning energy (e.g., light, electrons, etc.) over aphysical version of the specimen thereby generating output for thephysical version of the specimen. In this manner, the optical, electron,and ion beam subsystems may be configured as “actual” subsystems, ratherthan “virtual” subsystems. However, a storage medium (not shown) andcomputer subsystem(s) 102 shown in FIG. 1 may be configured as a“virtual” system. In particular, the storage medium and the computersubsystem(s) may be configured as a “virtual” inspection system asdescribed in commonly assigned U.S. Pat. No. 8,126,255 issued on Feb.28, 2012 to Bhaskar et al. and 9,222,895 issued on Dec. 29, 2015 toDuffy et al., both of which are incorporated by reference as if fullyset forth herein. The embodiments described herein may be furtherconfigured as described in these patents.

The one or more computer subsystems are configured for separatingpolygons in a care area on a specimen into initial sub-groups based on acharacteristic of the polygons on the specimen such that the polygonshaving different values of the characteristic are separated intodifferent initial sub-groups. For example, as shown in step 300 of FIG.3, the computer subsystem(s) may be configured for separating polygonsin a care area into initial sub-groups based on a characteristic of thepolygons on the specimen. Several examples of polygon characteristicsthat can be used for this step are described further herein. Howdifferent values of these characteristics are defined may vary in anumber of ways. For example, some characteristics are qualitative ratherthan quantitative, e.g., a square shape is different than a line shape,which are both different than an irregular polygon shape. However, manycharacteristics may be quantitatively different, e.g., orientationdifferences described in degrees, dimensions described in nm, areasdescribed in nm², etc. Therefore, whether two values of a characteristicare determined to be different in the separating step may be based on apredetermined range of values that define different values versus notdifferent values. The predetermined range of differences between thevalues may be determined in any suitable manner, e.g., a predeterminedrange may be set by a user, a predetermined range may be determinedstatistically based on how much two values have to be different for themto be considered statistically different, a predetermined range based ona priori knowledge of how different two polygons have to be to producestatistically different values of noise or how similar two polygons haveto be to produce statistically similar values of noise, etc.

Although the embodiments are described herein with respect to acharacteristic of the polygons, the separating step may be performedbased on one or more characteristics of the polygons such as shape,orientation, dimension, etc. Some of the polygons may be separated basedon values for one characteristic (e.g., shape), while other polygons maybe separated based on a different characteristic (e.g., orientation). Inone embodiment, the characteristic of the polygons includes a physicalcharacteristic of the polygons. In another embodiment, the separating isperformed by projecting the polygons along one axis. For example, thepolygons in care areas may be divided into initial sub-groups based ondesign polygon dimensions such as polygon area, x dimension, and ydimension, polygon orientation, polygon shape, projection value in xdirection or y direction, and care area dimensions. In this manner, theinitial sub-grouping of the polygons in a care area may be performedaccording to dimensions/shapes/orientations of the design polygonsand/or projection based groups.

One example of projection based analysis of polygons in a care area isshown in FIG. 4. FIG. 4 shows a design image for care area 400. Thedesign image shows the polygons in the design for the care area.Determining a characteristic of the polygons in this care area mayinclude projection along the y direction, which may produce projection402 showing the number of polygons as a function of position along the xaxis. Polygons having the same or similar counts may then be assigned tothe same care area initial sub-group. In other words, all designpolygons that are located in an area of high projection based count areaffiliated with care area initial sub-group 1 (CAG1) and the others withcare area initial sub-group 2 (CAG2). Therefore, based on the projectionshown in FIG. 4, polygons 404 in care area 400 will be separated intoCAG1 and polygons 402 in care area 400 will be separated into CAG2.Projection based separation of the polygons may be particularly suitablewhen a care area includes mostly line and space patterns with some otherpolygons in between.

FIG. 5 shows another example of care area 500 for a specimen such as awafer. As shown in FIG. 5, the care area includes multiple polygonshaving different characteristics. In particular, some of the polygonsare line-like structures that extend in the x direction, others of thepolygons are line-like structures that extend in the y direction, andsome additional polygons are square-like structures. Although FIG. 5(and other figures described herein) shows an example of a care areathat includes polygons in particular numbers and having particularcharacteristics, it is to be understood that the embodiments describedherein are not limited to any particular care areas and/or polygonshaving any particular characteristics (size, shape, location, etc.) forwhich the steps described herein can be performed. In addition, althoughthe embodiments are described herein with respect to a care area (ofwhich there may be multiple instances formed on a specimen), theembodiments described herein may also be separately and independentlyperformed for multiple care areas on a specimen.

The polygons in the care area shown in FIG. 5 may be separated intodifferent initial sub-groups as described further herein. For example,FIG. 7 shows version 700 of the care area of FIG. 5 with polygons havingdifferent characteristics shown with different fill patterns. Inparticular, the line-like structures that extend in the y direction areshown with horizontal line fill patterns, the line-like structures thatextend in the x direction are shown with vertical line fill patterns,and the square-like structures are shown with diagonal line fillpatterns. Each of the polygons shown in FIG. 7 having the same fillpattern, therefore, belongs in the same initial sub-group. In otherwords, the line-like structures that extend in the y direction may allbe separated into a first initial sub-group, the line-like structuresthat extend in the x direction may all be separated into a secondinitial sub-group, and the square-like structures may all be separatedinto a third initial sub-group. In this manner, the polygons shown inthe care area of FIG. 5 may be separated based on their shape, size,orientation, etc. into initial sub-groups.

In some embodiments, the polygons in the care area include polygons onmore than one layer of the specimen. For example, the polygons are notlimited to one single layer of polygons but can be extended to multiplelayers containing polygons. The more than one layer may include thelayer that will be inspected and a layer below the layer that will beinspected on the specimen. Therefore, the layer below the inspectedlayer may not necessarily be of interest in the inspection, but thatunderlying layer and/or polygons formed thereon may affect the outputgenerated for the specimen during the inspection. For example, theunderlying polygons may affect the noise in the output generated by aninspection subsystem for a specimen. Therefore, such polygons may betaken into account by the embodiments described herein since two of thesame polygons on the same inspected layer may have substantiallydifferent noise characteristics in inspection due to differentunderlying polygons. In this manner, the initial sub-groups may bedefined so that polygons on the inspected layer having different valuesof the characteristic are separated into different initial sub-groupsand so that polygons on the inspected layer having the same values ofthe characteristic but are located above one or more polygons havingdifferent values of a characteristic of the polygon(s) are separatedinto different initial sub-groups. In one such example, the line-likestructures that extend in the x direction shown in FIG. 5 may beseparated into an initial sub-group as shown in FIG. 7, then thepolygons in that initial sub-group may be further separated intosub-groups based on which polygons (not shown) the line-like structuresare formed above. Information about the polygons on more than one layerof the specimen may be acquired as described herein from a design forthe specimen. Defining the care area initial sub-groups can then beperformed for each layer independently or can be a combinationespecially when the design polygons of different layers are overlapping.

The creation of the care area initial and final sub-groups is also notlimited to splitting original care areas but entirely new care areas canbe created based on the design polygons and the characteristic of thenoise as described herein, respectively. For example, although forsimplicity, the design polygons in the care area and the design polygonsin the various sub-groups are the same in the examples shown herein,this does not have to be the case as the care areas may be grown orshrunk depending on the results of the steps described herein. In otherwords, a care area may be expanded from its original definition toencompass other nearby polygons having the same values of thecharacteristic of the polygons and substantially the same values of thecharacteristic of the noise as polygons in the originally defined carearea. In a similar manner, a care area may be shrunk from its originaldefinition to eliminate one or more polygons from the originaldefinition, which may be moved to another care area or may be removedfrom all care areas entirely based on the initial sub-grouping and finalsub-grouping steps described herein. Such expansion, shrinking, or othermodification to the perimeter defining a care area may be performedbased on only the polygons on the inspected layer or based on polygonson more than one layer of the specimen.

In another embodiment, the one or more computer subsystems are furtherconfigured for determining the characteristic of the polygons on thespecimen from a design for the specimen. For example, the separatingstep may include dividing care area groups into initial sub-groups byusing a design-based pattern search. In one such example, logic rulesbased on IC design, e.g., pattern density, line distance, etc., may beused to determine the characteristic of the polygons and to separatepolygons having different characteristics from each other. Thedesign-based pattern search may be performed in any suitable manner bythe embodiments described herein or by using another system or methodsuch as an electronic design automation (EDA) tool, which may includeany commercially available EDA tool known in the art.

In an additional embodiment, the one or more computer subsystems arefurther configured for determining the characteristic of the polygons onthe specimen by rendering a design for the specimen. For example, theseparating step may include a design rendering based approach in whichthe design for the specimen is used to simulate characteristics of thepolygons as they will be formed on the specimen. In particular, thecharacteristics of the polygons as they are designed may be differentfrom the characteristics of the polygons as they are formed on thespecimen. In addition, since it is the characteristics of the polygonsas formed on the specimen, not necessarily as they are designed, thatwill affect the characteristics of the noise in inspection systemoutput, the characteristics of the polygons as they will be formed onthe specimen may be more suitable than their as-designed characteristicsfor separating the polygons into initial sub-groups.

Rendering the design may include simulating what the design would looklike when printed or fabricated on a specimen. For example, renderingthe design may include generating a simulated representation of aspecimen on which the polygons are printed or formed. One example of anempirically trained process model that may be used to generate asimulated specimen includes SEMulator 3D, which is commerciallyavailable from Coventor, Inc., Cary, N.C. An example of a rigorouslithography simulation model is Prolith, which is commercially availablefrom KLA, and which can be used in concert with the SEMulator 3Dproduct. However, rendering the design for the specimen may be performedto generate a simulated specimen using any suitable model(s) of any ofthe process(es) involved in producing actual specimens from the design.In this manner, the design may be used to simulate what a specimen onwhich the design has been formed will look like in specimen space (notnecessarily what such a specimen would look like to an imaging system).Therefore, the design rendering may generate a simulated representationof the specimen that may represent what the specimen would look like intwo-dimensional (2D) or three-dimensional (3D) space of the specimen.

The computer subsystem(s) are also configured for determining acharacteristic of noise in output generated by a detector of aninspection subsystem for the polygons on the specimen in the differentinitial sub-groups. For example, as shown in step 302 of FIG. 3, thecomputer subsystem(s) may be configured for determining a characteristicof noise in output generated by a detector of an inspection subsystemfor the polygons. The output generated by the detector may include anyof the output described herein, e.g., image signals, image data,non-image signals, non-image data, etc. The characteristic of the noisemay be determined in a number of different ways described herein.

In some instances, the characteristic of the noise for the entire carearea can be substantially different from the characteristic of the noisefor different initial sub-groups of polygons within the care area, whichcan also be substantially different from each other. FIGS. 6 and 8illustrate such differences and show 2D histograms derived from awafer-based difference image of the structures within the care areaderived from the design polygons shown in FIGS. 5 and 7, respectively.The axes on the 2D histograms shown in these figures are the referencegrey level and the difference grey level. In FIG. 6, the reference greylevels and difference grey levels for all of the polygons in the carearea of FIG. 5 have been combined together. 2D histogram 600 shown inFIG. 6 is, therefore, of the original care area group (reference greylevel over difference grey level). At least one of the initialsub-groups of polygons is substantially noisy, which is shown in 2Dhistogram 600 by the substantially large dynamic range and substantiallyhigh noise level (where the “dynamic range” used in this context isdefined as the maximum-minimum grey level in a reference image frame).The dynamic range may be particularly useful for the characteristic ofthe noise in the embodiments described herein since it is usuallycorrelated to the types of polygons in the area for which the 2Dhistogram is generated (e.g., a care area that includes different typesof polygons, at least some of which have a different characteristic onthe specimen, will generally have a higher dynamic range in such a 2Dhistogram compared to a care area that includes only one type ofpolygons, all of which have substantially similar characteristics on thespecimen). For example, if there are lots of different patterns in areference image frame, i.e., a mix of patterns of different grey levels,then the 2D histogram may show a relatively high dynamic range.

As described above, after all of the individual polygons in the carearea shown in FIG. 5 are identified, each of the polygon shapes aregrouped into one of three initial sub-groups as shown in FIG. 7.Individual histograms may then be generated for each of the initialsub-groups. The individual histograms may be generated from the samedata that was used to generate histogram 600, e.g., by separating thedata based on where in the care area it was generated and where in thecare areas the polygons are located, the data can be separated fordifferent initial sub-groups of polygons. For example, as shown in FIG.8, 2D histogram 800 may be generated for the line-like structuresextending in the x direction, 2D histogram 802 may be generated for thesquare-like structures, and 2D histogram 804 may be generated for theline-like structures extending in the y direction. As can be seen fromFIG. 8, by evaluating the individual 2D histograms for the initialsub-groups, 2D histograms 802 and 804 show that the initial sub-groupsfor the square-like polygons and the line-like polygons extending in they direction are relatively quiet (e.g., have substantially low noisevalues in a relatively small dynamic range) with similar noisecharacteristics (e.g., substantially similar noise distributions acrosssubstantially similar values of the noise) whereas 2D histogram 800 forthe line-like polygons extending in the x direction is substantiallynoisy (e.g., has a relatively large dynamic range) with substantiallydifferent noise characteristics than the other 2D histograms (e.g., thenoise distribution in 2D histogram 800 is substantially different thanthose in 2D histograms 802 and 804). These histograms may then be usedto determine the final sub-groups of the polygons as described furtherherein.

In one embodiment, the output generated by the detector of theinspection subsystem used for determining the characteristic of thenoise is generated by scanning the specimen with the inspectionsubsystem. For example, using all of the care area initial sub-groups, awafer inspection run may be performed. The one or more computersubsystems may therefore be configured for acquiring the output used fordetermining the noise characteristic by using one of the inspectionsubsystems described herein (e.g., by directing light or an electronbeam to the specimen and detecting light or an electron beam from thespecimen). In this manner, acquiring the output may be performed usingthe physical specimen itself and some sort of inspection (e.g., imaging)hardware. However, acquiring the output does not necessarily includeimaging or scanning the specimen using imaging hardware. For example,another system and/or method may generate the output and may store thegenerated output in one or more storage media such as a virtualinspection system as described herein or another storage mediumdescribed herein. Therefore, acquiring the output may include acquiringthe output from the storage medium in which it has been stored.

In some embodiments, the output used for determining the characteristicof the noise is output generated with more than one mode of theinspection subsystem for the specimen. For example, multimodal noiseinformation may be used for the step(s) described further herein. Usingmultimodal noise may be beneficial when selecting defect detectionmethods for multi-mode inspection. The multiple modes of the inspectionsubsystem may include any of the modes described further herein. In someinstances, different noise characteristics may be determined from theoutput generated in different modes. For example, different modes may beused for the same inspection of a specimen, and the embodimentsdescribed herein may be configured to perform the steps separately formore than one mode, e.g., such that different final sub-groups aredetermined for different modes, for which different defect detectionmethods may then be selected as described herein. In this manner, whenthe modes are known, the steps described herein may be performed foreach of the modes separately and independently, and the output of thedetector(s) used for those steps may be generated in the same scan ofthe specimen or in multiple scans of the specimen, e.g., when the modescannot simultaneously be used to generate output for the specimen.

The steps described herein may also be performed for more than one modefor mode selection. For example, the steps described herein can be usedto evaluate different modes by performing the steps described herein fordifferent modes and then determining which mode or modes would be mostsuitable for inspection of a specimen, e.g., by comparing the finalsub-groups determined for different modes and the defect detectionmethods selected for the final sub-groups in the different modes, themode or mode combination that can detect the most DOIs, suppress themost nuisances, etc. can be identified and selected for use in theinspection of the specimen or other specimens of the same type. In thismanner, the embodiments described herein can be used for simultaneousselection of both mode(s) and algorithm(s). In addition, as describedfurther herein, parameter(s) of the defect detection methods that areselected can be independently tuned thereby enabling optimization of theinspection for each final sub-group and mode combination. Any otherparameter(s) of the inspection subsystem and/or inspection recipe canalso be independently selected for each mode/final sub-group/defectdetection method combination, e.g., nuisance filtering parameters,defect classification parameters, etc., by the embodiments describedherein. Such other parameters may be selected in any suitable mannerknown in the art.

In another embodiment, determining the characteristic of the noiseincludes performing statistical analysis of the output. For example, thenoise may be measured, and a statistical analysis may be performedcalculating the characteristics such as the standard deviation of thedifference grey level or the dynamic range. In addition, determining thecharacteristic of the noise may include performing a statisticalanalysis of the noise distribution of every individual care area initialsub-group, e.g., setting the offset of 0 at μ+/−3σ for the differencegrey level, wherein is the mean and σ is the standard deviation. Thecharacteristic of the noise may also or alternatively be acharacteristic of the noise relative to non-noise signals or image data.For example, the characteristic of the noise that is determined by theembodiments described herein may be a kind of collective signal-to-noiseratio that describes a characteristic of the noise relative to acharacteristic of non-noise. For example, the characteristic of thenoise may include or be determined based on the noise and/or outlier(potential defect signals or images) distribution characteristics.

In some embodiments, determining the characteristic of the noiseincludes determining the characteristic of the noise in the outputgenerated by the detector of the inspection subsystem for the polygonson the specimen in combination with output generated by the detector ofthe inspection subsystem for the polygons in the different initialsub-groups on another specimen. For example, the grouping for the finalsub-groups can be performed by collecting data on several (two or more)wafers. This way noisy and non-noisy care areas and polygons can beidentified with respect to wafer-to-wafer process variation. Inaddition, care area groups/sub-groups with relatively highwafer-to-wafer variation can be identified and grouped accordingly. Howmany specimens are scanned to generate the output used for determiningthe characteristic of the noise and determining the final sub-groups mayalso be determined dynamically. For example, if two specimens arescanned, and the same polygons on different specimens show substantiallyhigh variation in the noise characteristic from specimen-to-specimen,then one or more additional specimens may be scanned to furthercharacterize the noise exhibited by those polygons fromspecimen-to-specimen. Otherwise, the number of specimens that arescanned may be determined in any suitable manner known in the art.

In an additional embodiment, determining the characteristic of the noiseincludes determining the characteristic of the noise in the outputgenerated by the detector of the inspection subsystem for the polygonson the specimen in the different initial sub-groups in more than oneinstance of the care area on the specimen. For example, although theembodiments are described herein with respect to a care area and theymay be performed using output generated for a single care area instance,in general, the output that is used to determine the characteristic ofthe noise may be generated from more than one instance of the care areaon at least one specimen. The multiple instances of the care area may beformed in the same reticle instance, e.g., die, field, etc., on thespecimen and/or in more than one reticle instance on the specimen. Inaddition, the distribution of the care area instances across thespecimen used for determining the characteristic of the noise may bedetermined based on, for example, expected process variations across thespecimen which can affect the inspection subsystem output, the area onthe specimen that will be scanned during an inspection run, etc.Furthermore, although it may be advantageous to generate output frommany (many more than two) care area instances for determining the noisecharacteristic, generating output from all of the care area instances onthe specimen may not be necessary to fairly characterize the noise fromthe polygons in the specimen. Regardless of how the scanning or outputgeneration is performed, the output that is generated for the polygonsin the initial sub-groups can be identified in any suitable manner,e.g., based on the known positions of the polygons in the care area, theknown or estimated positions of the care area instances on the specimen,and the known positions at which the various output is generated on thespecimen. Therefore, the output that is generated for different polygonscan be separated and used to determine the characteristic of the noiseas described herein.

The computer subsystem(s) are configured for determining finalsub-groups for the polygons by combining any two or more of thedifferent initial sub-groups having substantially the same values of thecharacteristic of the noise into one of the final sub-groups. Forexample, as shown in step 304 of FIG. 3, the computer subsystem(s) maydetermine final sub-groups for the polygons. Depending on thecharacteristic of the noise, care area initial sub-groups may becombined. “Substantially the same” values, as that term is used herein,may be used interchangeably with the terms “insignificantly different”and “statistically similar” values. The term “statistically similar”values, as used herein, is intended to have the commonly accepteddefinition of the term used in the art of mathematics and in particularstatistics, i.e., statistically similar is commonly accepted to be“within the margin of error” and “not caused by something other thanchance.” Both of these definitions are consistent with the use of theterm herein. Whether or not values of the noise characteristic are“statistically similar” can be determined, for example, by comparing thedifferences in the values to the margin of error and determining thatdifferences within the margin of error are “statistically similar.”

In one such example, care area initial sub-groups with similar dynamicrange values, die-to-die (difference) grey level variation, etc. may becombined into one care area final sub-group. In FIG. 8, for example,since 2D histogram 800 generated for the sub-group of the line-likepolygons extending in the x direction is substantially different than 2Dhistograms 802 and 804 generated for the square-like polygons and theline-like polygons extending in the y direction, respectively,separating the line-like polygons extending in the x direction from theother polygons in the care area seems very promising since one couldlower the thresholds (vertical lines in the plots) for the otherpolygons to detect DOIs which are located at the center part of thenoise distribution. In addition, since 2D histograms 802 and 804generated for the square-like polygons and the line-like polygonsextending in the y direction, respectively, show that these polygonshave substantially the same noise characteristics, these polygons may becombined into one final sub-group. In this manner, determining the finalsub-groups may include combining initial sub-groups determined to havesubstantially the same noise behavior into a final sub-group. The muchnoisier initial sub-group that includes the line-like structuresextending in the x direction will therefore not be combined with theother polygon initial sub-groups and will instead be included in its ownseparate final sub-group.

In this manner, as shown in FIG. 8, since 2D histogram 800 generated forinitial sub-group A that includes line-like structures extending in thex direction is substantially different than the 2D histograms generatedfor other initial sub-groups, the line-line structures extending in thex direction may be included in one final sub-group, care area group 1indicated in FIG. 8 by reference numeral 806. In addition, since 2Dhistogram 802 generated for initial sub-group B that includessquare-like structures shows noise characteristics that aresubstantially the same as 2D histogram 804 generated for initialsub-group C that includes line-like structures that extend in the ydirection, these initial sub-groups may be combined into one finalsub-group, care area group 2 indicated in FIG. 8 by reference numeral808.

These final sub-groups are also shown in FIG. 9 in which the patternfills for polygons in the same final sub-group are the same. Inparticular, as shown in FIG. 9, since the square-like polygons and theline-like polygons extending in the y direction are combined into afinal sub-group, both of these polygons may be indicated with the samepattern fill, the horizontal lines shown in FIG. 9. In addition, sincethe line-like structures extending in the x direction are included intheir own final sub-group, these polygons are shown in FIG. 9 with adifferent pattern fill than all of the other polygon types in care areaversion 900, the vertical lines.

In one embodiment, determining the characteristic of the noise anddetermining the final sub-groups are implemented as a noise scanfunctionality on the one or more computer subsystems and the inspectionsubsystem. In one such embodiment, the one or more computer subsystemsand the inspection subsystem are configured for implementing the noisescan by collecting image frame data in the output generated by thedetector of the inspection subsystem and calculating difference imageframes according to a predetermined algorithm. In another suchembodiment, the predetermined algorithm is the same as a predeterminedalgorithm used by at least one of multiple defect detection methodssuitable for the inspection of the specimen or another specimen fromwhich the first and second defect detection methods are selected.

In this manner, the above-described determining a characteristic ofnoise and determining final sub-groups steps may be implemented as a“noise scan” functionality on the computer subsystem(s) and/or aninspection subsystem in which the two steps are performed in concertwith one another. In other words, if a user selects a “noise scan”option on the embodiments described herein, both the determining acharacteristic of noise and determining final sub-groups steps may beperformed by the embodiments described herein. Collectively, then, thesesteps may include collecting image frame data and calculating differenceimage frames according to a certain algorithm (whether that is bydie-to-die subtraction, cell-to-cell subtraction, test image to standardreference image subtraction, etc.). As such, the noise scan can beperformed using a defect detection algorithm of choice. Ideally, thesame defect inspection algorithm that was selected for the noise scanwill also be used for the actual defect detection (although that is notnecessarily required). The way the correct defect detection algorithm isselected may be based on the requirements of the inspection, e.g., somewafer setup makes it necessary to use SRD (described further herein)while others, due to severe process variation, may require MCAT+(alsodescribed further herein), and so on. In addition, the user may decidewhich type of algorithm to use in the noise scan or the defect detectionand in some instances one of the algorithms, e.g., MCAT (describedfurther herein), may be selected as a default. This difference imageframe will then be overlapped with sub-divided care area initialsub-groups to identify the correct final sub-grouping given theirindividual noise behavior.

The noise scan may also perform care area grouping using an algorithmthat minimizes systematic noise. For example, by organizing initialsub-groups of polygons into final sub-groups based on similarities anddifferences between the noise behavior of the initial sub-groups ofpolygons, the embodiments described herein can minimize systematic noisewithin the final sub-groups of polygons that are used for defectdetection. Using the final sub-groups as the care areas for inspection,therefore, provides significant advantages since as described furtherherein, defect detection performed for different final sub-groups may bedifferent and may be tailored to each of the final sub-groups and theirnoise behavior.

The embodiments described herein are not just limited to combininginitial sub-groups to determine the final sub-groups although that maybe more common. Determining the final sub-groups may in some instancesinclude separating polygons in an initial sub-group into different finalsub-groups. For example, different instances of identical polygons mayexhibit different noise behavior, e.g., depending on the polygons in theneighborhood of a polygon instance, the polygons underneath a polygoninstance, where on a specimen a polygon instance is located (e.g.,relative to an edge or a center of the specimen), etc. Therefore, in anoptional scenario, the computer subsystem(s) described herein may beconfigured to analyze the noise characteristics of the initialsub-groups to identify initial sub-group(s) having substantiallydifferent noise characteristics from polygon instance to polygoninstance.

In one such example, the computer subsystem(s) may compare noisecharacteristics determined for initial sub-groups such as dynamic rangeto a predetermined threshold and determine that any initial sub-groupshaving a dynamic range in excess of the predetermined threshold shouldbe evaluated for separation. The computer subsystem(s) may then separatethe polygon instances in various ways, e.g., depending on within carearea position, depending on within specimen position, depending onneighboring polygons, depending on underlying polygons, etc. Noisecharacteristics may then be determined for these initial“sub-sub-groups” or “intermediate sub-groups.” Depending on how similaror different those noise characteristics are, the polygon instances inthe initial sub-group may be separated into two or more different finalsub-groups.

In a hypothetical example, if 2D histograms are generated as describedherein for various intermediate sub-groups identified in one or more ofthe ways described above, and all of the 2D histograms look similar to2D histogram 600 shown in FIG. 6 (i.e., they all have substantiallysimilar and relatively large dynamic ranges), then the intermediatesub-groups may be recombined into their original initial sub-group anddesignated as a single final sub-group (unless they are being combinedwith another initial sub-group to form a final sub-group). In contrast,if 2D histograms are generated as described herein for two intermediatesub-groups identified in one or more of the ways described above, andone of the 2D histograms looks similar to 2D histogram 800 shown in FIG.8 and another of the 2D histograms looks similar to 2D histogram 802shown in FIG. 8, then the intermediate sub-groups may be assigned todifferent final sub-groups. In addition, determining the finalsub-groups for three or more intermediate sub-groups may include somecombination of assigning one intermediate sub-group to a final sub-groupand assigning two or more intermediate sub-groups to a different finalsub-group. Furthermore, intermediate sub-group noise characteristics maybe compared to the noise characteristics determined for other initialsub-groups (not just intermediate sub-groups), and based on similaritiesand differences between the noise characteristics, an intermediatesub-group may be combined with a sub-group into a final sub-group. Inthis manner, different subsets of polygons having the samecharacteristics on the specimen may be included in final sub-groups withother, non-similar polygons in terms of polygon characteristics. Finalsub-groups generated in such a way may otherwise be treated as describedfurther herein thereby allowing different instances of the same polygonto be inspected with different defect detection methods and/orparameters.

The computer subsystem(s) are further configured for selecting first andsecond defect detection methods for application to the output generatedby the detector of the inspection subsystem during inspection of thespecimen or another specimen of the same type for a first and a secondof the final sub-groups, respectively, based on the characteristic ofthe noise determined for the first and second of the final sub-groups,respectively. For example, as shown in step 306 of FIG. 3, the computersubsystem(s) may select defect detection methods for the finalsub-groups. The specimens for which the inspection is performed may beof the same type in that they may be subject to the same fabricationprocesses prior to having the inspection performed thereon.

Since the final sub-groups are determined as described herein so thatdifferent final sub-groups exhibit different noise characteristics, ingeneral, the defect detection methods selected for different finalsub-groups will most likely be different (although not necessarily forall final sub-groups). For example, the first and second defectdetection methods will in general be different from each other becausedifferent final sub-groups will in general have different noisecharacteristics. However, for two or more of the final sub-groups, thesame defect detection method may be selected in some instances. If thesame defect detection method is selected for two or more of the finalsub-groups, the parameters of the defect detection method may beindependently and separately tuned for each of the two or more finalsub-groups so that the defect detection method is tailored for differentfinal sub-groups.

The first and second defect detection methods may therefore be selectedseparately and independently for final sub-groups of polygons havingdifferent noise characteristics, which enables using the most sensitivedefect detection method possible for each of the final sub-groups. Forexample, the defect detection method that can detect the most DOIswithout prohibitive levels of nuisance detection may be independentlyselected for each of the final sub-groups based on the noisecharacteristics determined for each of the final sub-groups. Theinspection of the specimen or another specimen of the same type may theninclude defect detection performed using a combination of care areafinal sub-groups and defect detection methods or algorithms selected forthose care area final sub-groups. In this manner, the embodimentsdescribed herein provide sensitivity enhancement by care area divisionand re-grouping.

The first and second defect detection methods that are selected may becompletely different types of defect detection methods (not just thesame defect detection method with one or more different parameters suchas threshold). Both of the first and second defect detection methods mayalso include any suitable defect detection method known in the art, someexamples of which are described herein. For example, one of the firstand second defect detection methods may include a multi-die adaptivethreshold (MDAT) algorithm, which is available on some inspectionsystems commercially available from KLA. The MDAT algorithm performscandidate to reference image comparisons by image frame subtraction andidentifies outliers based on signal-to-noise through double detection(compares a candidate image to two reference images) or single detectionwhen compared to a median reference frame of more than two frames. Oneof the first and second defect detection methods may also oralternatively include a multi-computed die adaptive threshold (MCAT)algorithm, which is also available on some inspection systemscommercially available from KLA. In general, this defect detectionalgorithm is similar to the MDAT algorithm but optimizes the referenceto be similar to the test image frame before image subtraction isperformed. In addition, one of the first and second defect detectionmethods may include a MCAT+ algorithm, which is also available on somecurrently available inspection systems from KLA, and which is analgorithm similar to MCAT but uses references from across the wafer. Oneof the first and second defect detection methods may further include asingle reference die (SRD) defect detection method or algorithm, whichis available on some commercially available inspection systems from KLA.This defect detection algorithm uses a reference die from the same ordifferent wafer as a reference (for subtraction from test images).

In one embodiment, at least one of the first and second defect detectionmethods includes generating a one-dimensional (1D) histogram for theoutput generated by the detector of the inspection subsystem during theinspection. A defect detection method that generates a 1D histogram forthe detector output may be referred to as a 1D defect detection method.In one embodiment, the 1D histogram is generated from grey levels indifference images generated from the output generated by the detector ofthe inspection subsystem during the inspection. For example, a 1D defectdetection method or algorithm may use a 1D histogram for outlierdetection with the difference grey level on the x axis. The 1D histogrammay therefore show defect count over the difference grey level. In thismanner, the embodiments described herein may combine care areamodification with 1D defect detection, i.e., threshold setting based ona 1D image histogram. In contrast, a “2D defect detection algorithm” asthat term is used herein is an algorithm that uses a 2D histogram withone axis being, for example, the median grey level of n>1 referenceframes (y axis) and the x axis being the difference grey level (such asthe histograms shown in the figures described herein). In addition, thenoise scan performed for determining the characteristic of the noise asdescribed herein may match the selected defect detection method (i.e.,it may generate difference images, histograms, etc. in the same mannerthat a defect detection method would and determine the noisecharacteristics from those results).

The defect detection methods that are selected as described herein mayalso include a 1D analog for any 2D defect detection methods known inthe art. For example, every currently used 2D defect detection algorithmhas a 1D counterpart. In some such examples, the defect detectionalgorithms mentioned herein can have both 1D and 2D versions (e.g., 1DMDAT and 2D MDAT; 1D MCAT and 2D MCAT; 1D MCAT+ and 2D MCAT+; 1D SRD and2D SRD), and which version is selected for defect detection in any oneof the final sub-groups may be selected as described herein. The carearea modification described herein is therefore an enabler of 1D defectdetection.

In another embodiment, one of the first and second defect detectionmethods includes generating a 1D histogram for the output generated bythe detector of the inspection subsystem during the inspection, andanother of the first and second defect detection methods includesgenerating a 2D histogram for the output generated by the detector ofthe inspection subsystem during the inspection. For example, the firstand second defect detection methods may be selected to combine a 2Dhistogram defect detection method for final care area sub-groups withrelatively high dynamic range (e.g., group 1 shown in FIG. 9) and 1Ddefect detection for areas with relatively low dynamic range (e.g.,group 2 shown in FIG. 9). In other words, if the polygons can be dividedinto final sub-groups that all have a relatively low median grey levelrange and so that every final sub-group is substantially noise pure,then a 1D version of a defect detection method may be suitable for usewith the final sub-groups. If that is not the case or only some carearea final sub-groups are substantially noise pure, then the computersubsystem(s) may select a 2D version of a defect detection method forthe noise “impure” care area final sub-groups and a 1D version of adefect detection method for the noise “pure” care area final sub-groups.In addition, different versions of the same defect detection method maybe selected for different final sub-groups. For example, if there is arepeater defect in every printed instance of a reticle on a wafer, whichis the case for print check wafers, then the defect detection methodselected for two or more of the final sub-groups may be an SRD method oralgorithm since this defect detection method has a “golden”non-defective reference. For each of the two or more final sub-groupsfor which SRD is selected, a 1D or 2D version of the algorithm could beselected independently for each final sub-group depending on the noisepurity of each final sub-group.

In a further embodiment, the one or more computer subsystems areconfigured for separately tuning one or more parameters of the first andsecond defect detection methods. For example, for each care area finalsub-group, a threshold offset of 0 may be defined, e.g., based on theμ+/−3*σ. In one such example, the computer subsystem(s) may create ahistogram of the difference image, e.g., by plotting the number ofpixels over difference grey level. The computer subsystem(s) may thencalculate the statistical moments of this histogram such as μ and σ. Inmost cases, the μ should be substantially close to a difference greylevel of zero. If the σ value is at, say, 20 difference grey levels andthe threshold is to be set at 3 times σ, then this means that thethreshold will be at 60 difference image grey levels. Of course, this isjust an example and other statistical values can be used to set thethreshold.

The user can then specify which threshold should be applied whenperforming defect detection. For example, the user can select thethreshold value as they like if the default setting is not sufficient.The user could set the threshold value based on the number of outliersthat are detected using a certain threshold and if there is, forexample, a limit (e.g., a capture rate threshold).

As an alternative, the offset 0 calculation can be performed based onevery individual care area final sub-group and a threshold may beapplied to all of those. The offset set equal to zero refers to thedifference grey level where everything lower than that difference greylevel is considered noise and everything higher than that is consideredoutliers. Depending on the defect detection method that is used, thiszero value may be used instead of μ.

In this manner, user- or automatically-defined thresholds for the finalcare area sub-groups may be defined and applied during inspection. Inaddition, sensitivity tuning can be performed for different finalsub-groups separately and independently. Sensitivity tuning may also beperformed in any other suitable manner known in the art.

The computer subsystem(s) may also be configured for storing informationfor the selected first and second defect detection methods for use ininspection of the specimen or another specimen of the same type. Thecomputer subsystem(s) may be configured to store the information in arecipe or by generating a recipe for the inspection in which the firstand second defect detection methods will be applied. A “recipe” as thatterm is used herein is defined as a set of instructions that can be usedby a tool to perform a process on a specimen. In this manner, generatinga recipe may include generating information for how a process is to beperformed, which can then be used to generate the instructions forperforming that process. The information for the first and second defectdetection methods that is stored by the computer subsystem(s) mayinclude any information that can be used to identify, access, and/or usethe selected defect detection methods (e.g., such as a file name andwhere it is stored). The information for the selected defect detectionmethods that is stored may also include the actual defect detectionmethod code, instructions, algorithms, etc. for performing the defectdetection methods.

The computer subsystem(s) may be configured for storing the informationfor the selected defect detection methods in any suitablecomputer-readable storage medium. The information may be stored with anyof the results described herein and may be stored in any manner known inthe art. The storage medium may include any storage medium describedherein or any other suitable storage medium known in the art. After theinformation has been stored, the information can be accessed in thestorage medium and used by any of the method or system embodimentsdescribed herein, formatted for display to a user, used by anothersoftware module, method, or system, etc. For example, the embodimentsdescribed herein may generate an inspection recipe as described above.That inspection recipe may then be stored and used by the system ormethod (or another system or method) to inspect the specimen or otherspecimens to thereby generate information (e.g., defect information) forthe specimen or other specimens.

Results and information generated by performing the inspection on thespecimen or other specimens of the same type may be used in a variety ofmanners by the embodiments described herein and/or other systems andmethods. Such functions include, but are not limited to, altering aprocess such as a fabrication process or step that was or will beperformed on the inspected specimen or another specimen in a feedback orfeedforward manner. For example, the virtual system and other computersubsystem(s) described herein may be configured to determine one or morechanges to a process that was performed on a specimen inspected asdescribed herein and/or a process that will be performed on the specimenbased on the detected defect(s). The changes to the process may includeany suitable changes to one or more parameters of the process. Thevirtual system and/or other computer subsystem(s) described hereinpreferably determine those changes such that the defects can be reducedor prevented on other specimens on which the revised process isperformed, the defects can be corrected or eliminated on the specimen inanother process performed on the specimen, the defects can becompensated for in another process performed on the specimen, etc. Thevirtual system and other computer subsystem(s) described herein maydetermine such changes in any suitable manner known in the art.

Those changes can then be sent to a semiconductor fabrication system(not shown) or a storage medium (not shown) accessible to the virtualsystem or other computer subsystem(s) described herein and thesemiconductor fabrication system. The semiconductor fabrication systemmay or may not be part of the system embodiments described herein. Forexample, the virtual system, other computer subsystem(s), and/orinspection subsystem described herein may be coupled to thesemiconductor fabrication system, e.g., via one or more common elementssuch as a housing, a power supply, a specimen handling device ormechanism, etc. The semiconductor fabrication system may include anysemiconductor fabrication system known in the art such as a lithographytool, an etch tool, a chemical-mechanical polishing (CMP) tool, adeposition tool, and the like.

As described herein, therefore, the embodiments can be used to setup anew inspection process or recipe. The embodiments may also be used tomodify an existing inspection process or recipe, whether that is aninspection process or recipe that was used for the specimen or wascreated for one specimen and is being adapted for another specimen.However, the embodiments described herein are not just limited toinspection recipe or process creation or modification. For example, theembodiments described herein can also be used to setup or modify arecipe or process for metrology, defect review, etc. in a similarmanner. In particular, the separating polygons, determining acharacteristic of noise, and determining final sub-groups stepsdescribed herein can be performed regardless of the process that isbeing setup or revised. Then, depending on the process or recipe that isbeing setup or altered, the selecting step may be performed to selectone or more output processing methods for different final sub-groups.Such output processing methods may include, for example, algorithms usedto determine one or more characteristics of the polygons from outputgenerated by a metrology system, defect re-detection methods used forre-detecting defects in output generated by a defect review system, etc.In a similar manner, the embodiments described herein may be used toselect not just output processing parameters and methods but also outputacquisition parameters or modes, which with, for example, a metrologysystem or a defect review system detects light, electrons, ions, etc.from a specimen. The embodiments described herein can therefore be usednot just for setting up or modifying an inspection process but can beused for setting up or modifying any quality control type processperformed on the specimens described herein.

The embodiments described herein provide a number of advantages overpreviously used methods and systems for setting up inspection processesthat use care areas. For example, the embodiments described hereinprovide faster time to results as mode-algorithm combination decisionscan be made during the initial mode/algorithm selection process. Inaddition, the embodiments described herein can identify relatively noisyareas much more reliably and even relatively small, manually hard toidentify polygons can be sub-grouped correctly. Combining care areasinto groups exhibiting different noise characteristics also improvesoverall inspection sensitivity since enhanced sensitivity can beachieved for less noisy groups. Furthermore, the embodiments describedherein provide much better mitigation of within wafer and wafer-to-waferprocess variation as groups which are less impacted by wafer noisevariation are more stable. The embodiments described herein also allowincreasing the sensitivity to certain DOIs. This selective DOIsensitivity will allow users to improve their ability to make correctprocessing decisions based on results of the inspection.

As an alternative to the embodiments described herein, inspection setupcould include manually identifying noisy structures and performingdesign-based searches for the noisy structures to create new care areas.Such inspection setup could also include testing the new care area setupand finding additional noise sources. Such methods could then includegoing back and performing a design-based search for the newly identifiednoisy structures. However, unlike the embodiments described herein, thisprocess can take many days and is too slow and often insufficient interms of care area noise purity.

Each of the embodiments of each of the systems described above may becombined together into one single embodiment.

Another embodiment relates to a computer-implemented method forselecting defect detection methods for inspection of a specimen. Themethod includes the separating polygons, determining a characteristic,determining final sub-groups, and selecting first and second defectdetection methods steps described above.

Each of the steps of the method may be performed as described furtherherein. The method may also include any other step(s) that can beperformed by the inspection subsystem and/or computer subsystem(s) orsystem(s) described herein. The separating polygons, determining acharacteristic, determining final sub-groups, and selecting first andsecond defect detection methods steps are performed by one or morecomputer subsystems, which may be configured according to any of theembodiments described herein. In addition, the method described abovemay be performed by any of the system embodiments described herein.

An additional embodiment relates to a non-transitory computer-readablemedium storing program instructions executable on a computer system forperforming a computer-implemented method for selecting defect detectionmethods for inspection of a specimen. One such embodiment is shown inFIG. 10. In particular, as shown in FIG. 10, non-transitorycomputer-readable medium 1000 includes program instructions 1002executable on computer system 1004. The computer-implemented method mayinclude any step(s) of any method(s) described herein.

Program instructions 1002 implementing methods such as those describedherein may be stored on computer-readable medium 1000. Thecomputer-readable medium may be a storage medium such as a magnetic oroptical disk, a magnetic tape, or any other suitable non-transitorycomputer-readable medium known in the art.

The program instructions may be implemented in any of various ways,including procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the programinstructions may be implemented using ActiveX controls, C++ objects,JavaBeans, Microsoft Foundation Classes (“MFC”), SSE (Streaming SIMDExtension) or other technologies or methodologies, as desired.

Computer system 1004 may be configured according to any of theembodiments described herein.

Additional embodiments described herein are generally directed tosystems and methods for controlling a process for inspection of aspecimen, e.g. by statistical correction to mitigate loss in sensitivitydue to changing fabrication processing conditions on the specimen. Oneembodiment of a system configured for controlling a process forinspection of a specimen includes an inspection subsystem configured togenerate output responsive to energy detected from a specimen. Theinspection subsystem may be further configured as described herein(e.g., as shown by inspection subsystem 10 in FIG. 1 and the inspectionsubsystem shown in FIG. 2). In one embodiment, the inspection subsystemis a light-based inspection subsystem. In another embodiment, theinspection subsystem is an electron-based inspection subsystem. Suchinspection subsystems may be configured as described further herein(e.g., as shown in FIGS. 1 and 2). In one embodiment, the specimen is awafer. The specimen may include any of the wafer or other specimensdescribed herein.

The system includes one or more computer subsystems, which may beconfigured as any of the computer subsystems described herein (e.g.,computer subsystem 36 and computer subsystem(s) 102 shown in FIG. 1 andcomputer subsystem 124 shown in FIG. 2), configured for generatingdifference images for multiple instances of a care area on the specimenby subtracting a reference from the output corresponding to the multipleinstances of the care area. The difference images may be generated asdescribed further herein using any of the references described herein orknown in the art.

In one such embodiment, as shown in FIG. 11, the input to the one ormore computer subsystems may include candidate image 1100 and referenceimage 1102, which may include any of the candidate and reference imagesdescribed herein. One or more filters may be applied to each of theinput images. For example, the one or more computer subsystems may applyfilter 1104 to candidate image 1100 and filter 1106 to reference image1102. Filters 1104 and 1106 may include any suitable filters known inthe art such as intensity filters. For example, the filters may filterall high frequencies of an image (as in a high pass filter), filter alllow frequencies of an image (as in a low pass filter), or certainfrequency ranges of an image (as in a band pass filter). Filters 1104and 1106 may also be configured to perform the same type of filtering,such as intensity filtering, with the same or different parameters.Filters 1104 and 1106 are also optional and may not be used in someinspection processes.

The filtered candidate and reference images may then be input tosubtract step 1108 in which the reference image is subtracted from thecandidate image to thereby generate a preliminary or initial differenceimage. The results of the subtract step may be input to optionaldifference filter step 1110. The difference filter may be applied to thepreliminary or initial difference image meaning that the preliminary orinitial difference image is convolved with the matrix of the differencefilter. The difference filter may also include any other suitabledifference image filter known in the art. The results of the differencefilter step may be difference image 1112.

In the embodiments described herein, a difference image may be generatedfor each of the multiple instances of the care area on the specimen or adifference image may be generated for each of two or more of themultiple instances of the care area on the specimen, which may notinclude all of the multiple instances of the care area on the specimen.For example, for the embodiments described herein, all of the care areainstances on the specimen may be used for the steps described herein, oronly a portion of the care area instances on the specimen may beselected and used for the steps described herein (e.g., depending on howmany difference images are needed to determine a statisticalcharacteristic of the difference images described herein, depending onthe variation in the statistical characteristic that is beingdetermined, etc.). Therefore, for the purposes of controlling a processfor inspection of a specimen, the embodiments described herein maygenerate difference images for fewer than all of the care area instancesthat will be inspected in the process and may perform other stepsdescribed herein for fewer than all of the care area instances that willbe inspected in the process. How many and which of the care areainstances are used for controlling a process for inspection of aspecimen may be determined in a variety of ways such as the allottedtime and other resources available for performing the steps describedherein, how much variation is expected from specimen to specimen, howrobust the inspection process is to variation in the specimens, and thelike.

As described further herein, the term “multiple instances” of a “carearea” refers to multiple care areas of the same care area type, and allof the multiple instances of a care area type may be referred to as a“care area group.” For example, more than one of the same care area typemay be located in a die, field, or other reticle instance on a specimen.In addition, more than one type of care area may be located on aspecimen. In this manner, different care area groups may be generatedfor a specimen, and each of the care area groups may include one or moreinstances of a care area type. As described further herein, some stepsperformed for controlling the process for the inspection may beseparately and independently performed for different care areas, meaningdifferent care area types or different care area groups.

The care areas may be configured and generated as described furtherherein or may include any other suitable care areas known in the art.For example, in some embodiments, the one or more computer subsystemsare configured for separating polygons in the care area into initialsub-groups based on a characteristic of the polygons on the specimensuch that the polygons having different values of the characteristic areseparated into different initial sub-groups. The one or more computersubsystems are also configured for determining a characteristic of theoutput generated by the inspection subsystem for the polygons on thespecimen in the different initial sub-groups. In addition, the one ormore computer subsystems are configured for determining final sub-groupsfor the polygons by combining any two or more of the different initialsub-groups having substantially the same values of the characteristicinto one of the final sub-groups. In such embodiments, determining thestatistical characteristic, determining the variation, and determiningthe one or more changes are performed separately for each of the finalsub-groups. Each of these steps may be performed as described furtherherein. In another example, the one or more computer subsystems may beconfigured for setting up care areas as described in U.S. Pat. No.10,600,175 to Brauer et al. issued Mar. 24, 2020, which is incorporatedby reference as if fully set forth herein. The embodiments describedherein may be configured as described in this patent.

In one embodiment, the characteristic of the output includes acharacteristic of the output determined for detecting the defects. Inanother embodiment, the characteristic of the output includes acharacteristic of noise in the output. For example, using sub-dividedcare area groups which, when generated as described herein, aresubstantially similar in terms of their defect attributes such as greylevel range of 2D histogram, MDAT/MCAT noise, etc., the one or morecomputer subsystems may combine care areas into care area groups. Carearea setup can be performed on the setup specimen but performing it on aruntime specimen is possible as well. In this manner, the one or morecomputer subsystems may combine sub-groups of care area instances havingthe same noise behavior into the same care area groups. In addition,different care area groups may include care area instances havingstatistically different values of the characteristic of noise such asstandard deviation.

The one or more computer subsystems are also configured for determininga statistical characteristic of the difference images for the multipleinstances of the care area. In one embodiment, the statisticalcharacteristic is a standard deviation of grey level of all pixels ofthe output in the multiple instances of the care area. For example, theone or more computer subsystems may perform a statistical analysis basedon the difference grey levels of all pixels in each care area group tocalculate the care area group based standard deviation. The statisticalanalysis may be performed using any suitable method, function,algorithm, etc. known in the art. In this manner, the embodimentsdescribed herein may be configured for calculating a care area groupspecific standard deviation of difference images. Although some examplesand embodiments are described herein with respect to a standarddeviation of the difference images, the statistical characteristic mayinclude any other suitable statistical characteristic known in the art.

In another embodiment, the statistical characteristic is responsive tochange in the specimen caused by variation in the process performed onthe specimen prior to generation of the output. For example, the processperformed on the specimen prior to generation of the output may includeany fabrication process that may be performed on the specimens describedherein such as lithography, etch, chemical mechanical polishing,deposition, and the like. Such processes may inadvertently cause changesin the characteristics of the specimens from specimen to specimen aswhen the process drifts. Changes to the characteristics of the specimensmay also occur when the process is changed intentionally, whether thatis to control the process with the intention of not causing changes inthe specimens or to purposefully cause changes in the specimens. Forexample, when one tool is switched out for another tool, even if theprocess performed on the specimens is intended to be the same, the toolsmay perform differently thereby causing changes in the specimens. Inanother example, when a calibration or process control adjustment ismade to a fabrication process, that calibration or control function cancause the specimens subject to that fabrication process to be different,whether or not those differences are significant enough to cause actualdefects on the specimens. In another example, a fabrication process maybe changed to intentionally change one or more characteristics of thespecimens being produced by that process. As such, the intentionalchanges in the one or more characteristics of the specimens may bedetected as defects by a previously setup inspection process even thoughthey are not actual defects.

The statistical characteristic that is determined by the embodimentsdescribed herein is therefore preferably responsive to change in thespecimen caused by variation in the process performed on the specimenprior to generation of the output. As such, the statisticalcharacteristic that is suitable for use in the embodiments describedherein may vary depending on the specimen configuration and theprocess(es) performed on it prior to inspection. Identifying andselecting such a statistical characteristic may be performed in anysuitable manner known in the art (e.g., experimentally ortheoretically).

The one or more computer subsystems are further configured fordetermining variation in the statistical characteristic compared to astatistical characteristic of difference images generated for multipleinstances of the care area on one or more other specimens. In thismanner, the calculated statistical characteristic such as a standarddeviation may be used to track the value to study specimen-to-specimenprocess variation. In one such example, the embodiments described hereinmay detect a specimen change based on standard deviation.

There are many ways that the variation can be determined and trackedfrom specimen to specimen. For example, FIG. 12 shows one example ofwhisker plot 1200 that may be used to track the statisticalcharacteristic from specimen to specimen, in this case from wafer towafer. To generate these results, wafers 1 and 2 were fabricated with adifferent process (process A) than wafers 3 and 4 (fabricated withprocess B). In addition, the whisker plot may be generated from pixelswithin one single CA group (i.e., multiple instances of only one CA typeon the specimen).

The whisker plot shown in FIG. 12 is generated by plotting differencegrey level (DGL) as a function of mean and mean+/−3*standard deviation(i.e., μ+3σ, μ, and μ−3σ) and as a function of specimen. The plot canthen be used to determine significant or meaningful changes in the DGLas a function of one or more of μ+3σ, μ, and μ−3σ. For example, as canbe seen by comparing the μ+3σ values of wafers 2 and 3, i.e., values1202, there is a statistically meaningful jump in μ+3σ when moving fromwafer 2 to wafer 3, which may occur after the process change describedabove (from process A used for wafers 1 and 2 to process B used forwafers 3 and 4). A similar statistically meaningful jump (in theopposite direction) can be seen in μ−3σ when moving from wafer 2 towafer 3. Therefore, the wafer to wafer variation can be seen whencomparing the μ+3 σ and μ−3 σ DGL of wafer 2 to wafer 3.

Portions of the whisker plot can also be extracted for further analysis.For example, each whisker in the example of FIG. 12 contains 7 pointswith one of them being from the outer edge of the wafer (outlier inplot). Each whisker therefore shows the variation of σ within a wafer.In one such example, exploded view 1204 shows a whisker with the μ−3σdata points determined for wafer 3, which includes 1 data point from anedge die on the wafer and 6 data points from non-edge dies. As can beseen from this exploded view, outlier 1206 from the edge of the wafercan be easily detected. Therefore, the individual whiskers of a whiskerplot such as that shown in FIG. 12 can be used to determine variation inthe statistical characteristic within or across a wafer or otherspecimen. In this manner, whisker plots can be used to compare anddetect both within wafer and wafer to wafer variations in thestatistical characteristic.

The one or more computer subsystems are also configured for determiningone or more changes to one or more parameters used for detecting defectsin the care area on the specimen based on the variation. For example, asshown in FIG. 11, the one or more computer subsystems may use differenceimage 1112 as input to a care area (CA) group depending processvariation correction step 1114, which may include performing thedetermining the statistical characteristic, determining the variation,and determining the one or more changes steps described further herein.In this manner, the embodiments described herein may be configured forcalculating a CA group specific process variation adjustment parameter.

In one embodiment, the one or more parameters include a normalizationapplied to the difference images prior to using the difference imagesfor detecting the defects. For example, the calculated standarddeviation may be used for normalization of features such as DGL which isused for defect detection. The embodiments described herein may alsodetermine one or more changes to one or more parameters of otherprocesses, steps, functions, etc. performed to generate the differenceimages. For example, the embodiments described herein may determine oneor more changes to one or more parameters of filtering steps 1104 and1106 shown in FIG. 11 and difference filter 1110 shown in FIG. 11.

In this manner, the embodiments described herein may be configured foradjusting difference images based on a process variation parameter. Inother words, by changing one or more parameters of one or more steps orfunctions used to generate the difference images, the embodiments canchange the difference images themselves. In addition, by changing theparameter(s) used to generate the difference images, the non-defectvariation in the difference images generated for one specimen comparedto another specimen can be reduced, and even minimized or eliminated.Such a normalization of the difference images for a specimen to thosegenerated for one or more previous specimens may therefore eliminate theneed to change any other parameter(s) of the defect detection process,such as a threshold that is applied to the values or characteristics ofthe pixels in the difference images to detect defects on the specimen.However, as described further herein, the embodiments are not limited tochanging only parameter(s) of one step of the inspection process. Forexample, the embodiments described herein may determine that one or morechanges should be made to both a normalization and/or other process stepperformed to generate the difference images as well as one or morefunctions or steps performed on the difference images to detect defectson the specimen.

In another embodiment, the one or more changes to the one or moreparameters change a sensitivity with which the defects are detected. Forexample, whether or not the one or more changes to the parameter(s) usedfor detecting defects include one or more changes to the parameter(s)used for generating the difference images, the one or more changes tothe parameter(s) may include change(s) to the defect detection method oralgorithm that control(s) the sensitivity with which defects aredetected in the CA instances. For example, the sensitivity may bechanged by altering a threshold of a defect detection method oralgorithm. In this manner, if inspection process output is more noisydue to changes in a specimen compared to specimen(s) used to setup theinspection process, the sensitivity of the defect detection can bechanged so that the noise is not detected as nuisances and/or does notcause DOIs to go undetected.

In some embodiments, the one or more parameters include one or moreparameters of the inspection subsystem used to generate the output. Forexample, the one or more computer subsystems may consider changing oneor more illumination parameters of the inspection subsystem such asillumination and gain in response to the variation in the statisticalcharacteristic determined by the one or more computer subsystems. Ingeneral, the one or more parameters may include any one or moreparameters of the inspection subsystem described herein. For example,the noise in the inspection subsystem output can also or alternativelybe reduced or mitigated by changing one or more parameters of thedetection subsystem. The one or more changes to the parameter(s) of theinspection subsystem may be determined in any suitable manner known inthe art (e.g., experimentally or theoretically).

Of course, if the one or more computer subsystems determine that thereis no, minimal, or acceptable variation in the statisticalcharacteristic, the one or more computer subsystems may determine thatthe one or more changes are null, meaning that the one or more computersubsystems may determine that the one or more changes are zero or thatthere are no one or more changes. In other words, determining the one ormore changes may include determining that the changes(s) are effectivelyzero. Whether or not the one or more changes are non-zero may includenot just determining if there is some non-zero variation in thestatistical characteristic but also determining if the non-zerovariation in the statistical characteristic is large enough to warrantchanging the one or more parameters. Determining if the variation in thestatistical characteristic is sufficiently large that one or morechanges should be made to the one or more parameters used for detectingthe defects may be performed in a variety of ways such as by comparingthe variation in the statistical characteristic to a predeterminedthreshold determined based on, for example, the tolerance of theparameters of the inspection process to changes in the specimen, whichmay be determined during setup of the inspection process.

The one or more changes may include quantitative and/or qualitativechanges to the parameter(s) of the inspection process. For example, theone or more changes may include a qualitative change such as changingfrom one light source or illumination channel to another or changing thefiltering performed on the difference images by swapping out onedifference filter for another. However, the one or more changes may alsoor alternatively include quantitative change(s) such as changing a valueof a threshold in a defect detection algorithm used for detecting thedefects, determining a quantitative adjustment to the gain of anillumination channel, and the like. These qualitative and/orquantitative changes may be otherwise determined and implemented in anysuitable manner known in the art.

The embodiments described herein therefore can use a statistical basedcorrection to mitigate loss in sensitivity due to changing processingconditions. For example, when there is variation from specimen tospecimen inspected in the same process, the variation can cause thesensitivity to be reduced for a number of reasons. In one such example,when an inspection process is setup for specimen(s) having the samecharacteristics and/or characteristics in one range of values, and theinspection process is then used for specimen(s) having differentcharacteristics and/or characteristics outside of that range of values,the differences between the specimens used for setup and the specimensbeing inspected can cause a significant number of nuisances to bedetected and/or for DOIs to be missed. For example, even if thedifferences between the setup specimen(s) and the inspected specimen(s)are not due to defects on the inspected specimen(s), those differencesmay cause the inspection process to detect a significant amount of noisein the output generated by the inspection subsystem in the inspectionprocess. That significant amount of noise can be detected as nuisancesand/or cause the DOIs to go undetected. However, by controlling aninspection process as described herein for changes from specimen tospecimen, one or more parameters of the inspection process can bealtered so that the changes from specimen to specimen are not detectedas noise and/or do not cause DOIs to go undetected. In this manner, theembodiments described herein can advantageously make defect inspectionmore robust to fabrication process variation thereby making sure thatthe inspection can produce reliable specimen defect density data evenwhen the process changed or has been changed.

In one embodiment, generating the difference images, determining thestatistical characteristic, determining the variation, and determiningthe one or more changes are separately performed for the care area andanother care area on the specimen. For example, generating thedifference image step may be performed in the same manner for differentcare area types or groups on the specimen, even if different portions ofa reference are used for generating difference images for different carearea types or groups. However, generating the difference images fordifferent care area types or groups may also be separately andindependently performed such as when different intensity filters,different difference image filters, different normalizations, etc. areused to generate the difference images for different care area types orgroups. The other steps described herein may however be separately andindependently performed for different care area types or groups becausedifferent care area types or groups may be differently impacted bychanges in the fabrication process(es) performed on the specimens. Forexample, patterned features in one care area type may not be affected atall by a change in a fabrication process while patterned features inanother care area type may be significantly altered by the same changein the fabrication process. As such, even if different care area typesexperience the same variation in a fabrication process, the determinedstatistical characteristic for the different care area types may bedifferent and vary differently from specimen to specimen and benefitfrom different changes to the inspection process parameter(s). Otherthan being separately and independently performed, the steps describedherein may be performed in the same manner described herein fordifferent care area types. In this manner, the embodiments describedherein can optimize defect inspection performed on a care area grouplevel.

In another embodiment, a recipe for the process for the inspection issetup prior to generating the difference images, determining thestatistical characteristic, determining the variation, and determiningthe one or more changes. In other words, although the embodimentsdescribed herein may be used to setup an inspection process fromscratch, the embodiments were created primarily to monitor theperformance of a previously setup inspection process. For example,setting up an inspection process and monitoring an inspection processmay include some of the same steps but in general the setup phase willinclude many more steps than the monitoring phase and some steps in themonitoring phase may be performed differently than in the setup phase.In one such example, setting up an inspection process may include manymore measurements on fewer specimens while monitoring the inspectionprocess may include significantly fewer measurements on many morespecimens (e.g., each inspected specimen). In this manner, an inspectionprocess may be setup based on statistical variation across a fewspecimens but inspection process monitoring as described herein isperformed based on statistical variation from specimen to specimen.Inspection process setup and monitoring can have additional differencesin the performance of these methods. As such, inspection process setupand monitoring may be different not only in the steps that are involvedbut also in the motivation for performing the processes.

In a further embodiment, generating the difference images, determiningthe statistical characteristic, determining the variation, anddetermining the one or more changes are performed for each specimen onwhich the process for the inspection is performed. For example, theembodiments described herein can track especially wafer-to-wafer processvariation by performing a statistical analysis on each of the specimens.In addition, the calculations described herein may be performed on everywafer during runtime while defect inspection is being performed.Monitoring the variation and determining the one or more changes fromspecimen-to-specimen can therefore detect and correct for processvariations in the specimens as soon as they happen.

In some embodiments, the one or more computer subsystems are configuredfor determining variation in a fabrication process performed on thespecimen compared to the fabrication process performed on the one ormore other specimens based on the variation. In this manner, thecalculated statistical characteristic such as a standard deviation maybe used to track the value to study specimen-to-specimen processvariation. The one or more computer subsystems may determine if thereare process variations in a number of different ways. For example, ifthe variation in the statistical characteristic is determined to besignificant, the computer subsystem(s) may simply detect that variationin the fabrication process has occurred. However, the one or morecomputer subsystems may also use the determined variation in thestatistical characteristic to determine a quantitative value for thevariation in the fabrication process. For example, during setup of thefabrication process, establishing the parameters that are used for thefabrication process typically involves determining some relationshipbetween the characteristics of the processed specimens and the values ofthe parameters. In one such example, determining what values of exposurefocus and dose to use for a lithography process generally involvesdetermining how different values of the focus and dose affect thecharacteristics of the specimen. Such a relationship could then be madeavailable to the embodiments described herein by a user so that thevariation in the statistical characteristic and the relationship can beused to determine what values of the parameters were used to fabricatethe specimen. In this way, not only drift in the fabrication process canbe detected, but the cause of the drift can be identified in aquantifiable manner.

In an additional embodiment, the one or more computer subsystems areconfigured for determining additional variation in the statisticalcharacteristic as a function of position of the multiple instances ofthe care area on the specimen and determining variation in a fabricationprocess performed on the specimen based on the additional variation inthe statistical characteristic. In this manner, the one or more computersubsystems may track the calculated statistical characteristic such as astandard deviation to study within specimen process variation. Forexample, as described further herein, a whisker plot such as that shownin FIG. 12 may be used to track changes in within specimen variation inthe statistical characteristic as well as specimen-to-specimen variationin the statistical characteristic.

Determining within or across specimen variation in the statisticalcharacteristic can be advantageous for a number of reasons. For example,the parameter(s) of the inspection process may be controllable as afunction of position on the specimen based on the within specimenvariation, which means that the sensitivity and performance of theinspection process can be maintained across the specimen. In addition,the variation in the statistical characteristic across the specimen canbe particularly useful for determining variation in a fabricationprocess performed on a specimen. In one such example, some fabricationprocess variations can cause spatial signatures in characteristic(s) ofthe specimens produced by the fabrication process. In a particularexample, some changes in an etch process can cause one type of spatialvariation in a statistical characteristic of a specimen like a linearchange in the statistical characteristic from left to right while otherchanges in the etch process can cause another type of spatial variationin the statistical characteristic of the specimen like a nonlinearchange across a radius of the specimen. Therefore, identifying how thestatistical characteristic of the specimen varies across the specimencan increase the accuracy with which the variation in the fabricationprocess can be detected.

The one or more computer subsystems may further be configured forapplying the one or more changes to the one or more parameters used fordetecting the defects and performing the defect detection after the oneor more changes have been applied to the one or more parameters. Forexample, as shown in step 1116 of FIG. 11, the one or more computersubsystems may perform defect detection and derive defect attributesusing the changed one or more parameters. Applying the one or morechanges may be performed in a number of different ways such as causingone or more changes in the inspection subsystem hardware (e.g., anillumination and/or detection subsystem) or making one or more changesin the computer subsystem hardware or software (e.g., altering analgorithm used for defect detection). Applying the one or more changesmay also include making one or more changes to a recipe for theinspection process, which can then be performed on the specimen and anyother subsequent specimens. Changing a recipe for the inspection processmay be performed as described herein. In this manner, the one or morechanges may be made to one or more elements of the system or one or moresteps performed by the one or more computer subsystems simply by storingthe changes in an inspection process recipe. Defect detection using thechanged inspection process may be performed in any manner describedherein. In other words, once the change(s) have been made to theparameter(s) of the inspection process, the inspection process can beperformed as it would normally for defect detection on a specimen.

In one embodiment, detecting the defects includes generating a 1Dhistogram of pixel count as a function of grey level of pixels in thedifference images. In another embodiment, detecting the defects includesgenerating a 2D histogram of pixel count as a function of grey level ofpixels in the reference and grey levels of the pixels in the differenceimages. In this manner, detecting the defects may include 1D or 2D typedefect detection, which may be performed as described further herein. Inthis manner, the detection of the outliers does not need to be performedbased on a 2D histogram (pixel count over reference grey level anddifference grey level) but could also be performed based on a 1Dhistogram (pixel count over difference grey level). As long as thepurity in each care area group is guaranteed, 1D type defect detectionis possible. In this manner, the embodiments described herein makeperforming outlier detection based on a 1D histogram possible. If not,the detection may be performed based on a 2D histogram as usual.

The defect detection may also be performed by the embodiments describedherein as described in U.S. Pat. No. 10,151,706 to Bhattacharyya et al.issued Dec. 11, 2018, U.S. Pat. No. 10,192,302 to Bhattacharyya et al.issued Jan. 29, 2019, and U.S. Pat. No. 10,535,131 to Brauer et al.issued Jan. 14, 2020, which are incorporated by reference as if fullyset forth herein. The embodiments described herein may be furtherconfigured as described in these patents.

The embodiments described herein have a number of advantages over othermethods and systems for controlling a process for inspection of aspecimen. For example, by using the embodiments described herein tomonitor and control the inspection process, no or only substantiallylimited re-tuning of recipes is required when the wafer fabricationprocess changes. In another example, the embodiments described hereinallow more correct DOI count predictions. Furthermore, the embodimentsdescribed herein enable defect detection to be performed after acorrection for changes in specimen condition as well as changingillumination parameters are considered. The embodiments described hereinalso allow the sensitivity to certain DOIs to be increased when thespecimens change. This ability will allow users to improve their abilityto make correct processing decisions based on the detected defects.

Each of the embodiments of each of the systems described above may becombined together into one single embodiment.

Another embodiment relates to a computer-implemented method forcontrolling a process for inspection of a specimen. The method includesgenerating difference images for multiple instances of a care area on aspecimen by subtracting a reference from output corresponding to themultiple instances of the care area. The output is generated by aninspection subsystem and is responsive to energy detected from thespecimen. The inspection subsystem may be configured as describedfurther herein. The method also includes the determining a statisticalcharacteristic, determining variation, and determining one or morechanges steps described above.

Each of the steps of the method may be performed as described furtherherein. The method may also include any other step(s) that can beperformed by the inspection subsystem and/or computer subsystem(s) orsystem(s) described herein. Generating the difference images,determining the statistical characteristic, determining the variation,and determining the one or more changes are performed by one or morecomputer subsystems, which may be configured according to any of theembodiments described herein. In addition, the method described abovemay be performed by any of the system embodiments described herein.

An additional embodiment relates to a non-transitory computer-readablemedium storing program instructions executable on a computer system forperforming a computer-implemented method for controlling a process forinspection of a specimen. One such embodiment is shown in FIG. 10. Inparticular, as shown in FIG. 10, non-transitory computer-readable medium1000 includes program instructions 1002 executable on computer system1004. The computer-implemented method may include any step(s) of anymethod(s) described herein. The computer-readable medium, programinstructions, and computer system may be further configured as describedabove.

Further modifications and alternative embodiments of various aspects ofthe invention will be apparent to those skilled in the art in view ofthis description. For example, methods and systems for controlling aprocess for inspection of a specimen are provided. Accordingly, thisdescription is to be construed as illustrative only and is for thepurpose of teaching those skilled in the art the general manner ofcarrying out the invention. It is to be understood that the forms of theinvention shown and described herein are to be taken as the presentlypreferred embodiments. Elements and materials may be substituted forthose illustrated and described herein, parts and processes may bereversed, and certain features of the invention may be utilizedindependently, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the invention. Changes may bemade in the elements described herein without departing from the spiritand scope of the invention as described in the following claims.

What is claimed is:
 1. A system configured for controlling a process forinspection of a specimen, comprising: an inspection subsystem configuredto generate output responsive to energy detected from a specimen; andone or more computer subsystems configured for: generating differenceimages for multiple instances of a care area on the specimen bysubtracting a reference from the output corresponding to the multipleinstances of the care area; determining a statistical characteristic ofthe difference images for the multiple instances of the care area;determining variation in the statistical characteristic compared to astatistical characteristic of difference images generated for multipleinstances of the care area on one or more other specimens; anddetermining one or more changes to one or more parameters used fordetecting defects in the care area on the specimen based on thevariation.
 2. The system of claim 1, wherein the one or more computersubsystems are further configured for: separating polygons in the carearea into initial sub-groups based on a characteristic of the polygonson the specimen such that the polygons having different values of thecharacteristic are separated into different initial sub-groups;determining a characteristic of the output generated by the detector ofthe inspection subsystem for the polygons on the specimen in thedifferent initial sub-groups; and determining final sub-groups for thepolygons by combining any two or more of the different initialsub-groups having substantially the same values of the characteristicinto one of the final sub-groups, wherein determining the statisticalcharacteristic, determining the variation, and determining the one ormore changes are performed separately for each of the final sub-groups.3. The system of claim 2, wherein the characteristic of the outputcomprises a characteristic of the output determined for detecting thedefects.
 4. The system of claim 2, wherein the characteristic of theoutput comprises a characteristic of noise in the output.
 5. The systemof claim 1, wherein the statistical characteristic is a standarddeviation of grey level of all pixels of the output in the multipleinstances of the care area.
 6. The system of claim 1, wherein thestatistical characteristic is responsive to changes in the specimencaused by variation in a process performed on the specimen prior togeneration of the output.
 7. The system of claim 1, wherein the one ormore parameters comprise a normalization applied to the differenceimages prior to using the difference images for detecting the defects.8. The system of claim 1, wherein the one or more changes to the one ormore parameters change a sensitivity with which the defects aredetected.
 9. The system of claim 1, wherein the one or more parameterscomprise one or more parameters of the inspection subsystem used togenerate the output.
 10. The system of claim 1, wherein generating thedifference images, determining the statistical characteristic,determining the variation, and determining the one or more changes areseparately performed for the care area and another care area on thespecimen.
 11. The system of claim 1, wherein a recipe for the processfor the inspection is setup prior to generating the difference images,determining the statistical characteristic, determining the variation,and determining the one or more changes.
 12. The system of claim 1,wherein generating the difference images, determining the statisticalcharacteristic, determining the variation, and determining the one ormore changes are performed for each specimen on which the process forthe inspection is performed.
 13. The system of claim 1, wherein the oneor more computer subsystems are further configured for determiningvariation in a fabrication process performed on the specimen compared tothe fabrication process performed on the one or more other specimensbased on the variation.
 14. The system of claim 1, wherein the one ormore computer subsystems are further configured for determiningadditional variation in the statistical characteristic as a function ofposition of the multiple instances of the care area on the specimen anddetermining variation in a fabrication process performed on the specimenbased on the additional variation in the statistical characteristic. 15.The system of claim 1, wherein detecting the defects comprisesgenerating a one-dimensional histogram of pixel count as a function ofgrey level of pixels in the difference images.
 16. The system of claim1, wherein detecting the defects comprises generating a two-dimensionalhistogram of pixel count as a function of grey level of pixels in thereference and grey level of the pixels in the difference images.
 17. Thesystem of claim 1, wherein the specimen is a wafer.
 18. The system ofclaim 1, wherein the inspection subsystem is a light-based inspectionsubsystem.
 19. The system of claim 1, wherein the inspection subsystemis an electron-based inspection subsystem.
 20. A non-transitorycomputer-readable medium, storing program instructions executable on acomputer system for performing a computer-implemented method forcontrolling a process for inspection of a specimen, wherein thecomputer-implemented method comprises: generating difference images formultiple instances of a care area on a specimen by subtracting areference from output corresponding to the multiple instances of thecare area, wherein the output is generated by an inspection subsystemand is responsive to energy detected from the specimen; determining astatistical characteristic of the difference images for the multipleinstances of the care area; determining variation in the statisticalcharacteristic compared to a statistical characteristic of differenceimages generated for multiple instances of the care area on one or moreother specimens; and determining one or more changes to one or moreparameters used for detecting defects on the specimen in the care areabased on the variation.
 21. A computer-implemented method forcontrolling a process for inspection of a specimen, comprising:generating difference images for multiple instances of a care area on aspecimen by subtracting a reference from output corresponding to themultiple instances of the care area, wherein the output is generated byan inspection subsystem and is responsive to energy detected from thespecimen; determining a statistical characteristic of the differenceimages for the multiple instances of the care area; determiningvariation in the statistical characteristic compared to a statisticalcharacteristic of difference images generated for multiple instances ofthe care area on one or more other specimens; and determining one ormore changes to one or more parameters used for detecting defects on thespecimen in the care area based on the variation, wherein generating thedifference images, determining the statistical characteristic,determining the variation, and determining the one or more changes areperformed by one or more computer subsystems.